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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,742 papers · 148 categories

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6 results for “Rare-Switching”

Achieving Limited Adaptivity for Multinomial Logistic Banditscs.LG

Two algorithms achieve optimal regret with limited adaptivity in multinomial logistic bandits.

problem Achieving optimal regret with limited adaptivity in multinomial logistic bandits.

method Presented two algorithms, B-MNL-CB and RS-MNL, for batched and rarely-switching paradigms.

result Achieved

ilde{O}(\sqrt{T})

regret with limited adaptivity.

multinomial logistic bandits regret minimization limited adaptivity batched setting rarely-switching setting

2025-08-05abs ↗pdf ↗

Nearly Minimax Optimal Reinforcement Learning for Linear Markov Decision Processescs.LG

New RL algorithm achieves nearly optimal performance for linear MDPs.

problem Optimal reinforcement learning for episodic linear MDPs.

method Weighted linear regression with variance estimator and rare-switching policy.

result Achieves nearly minimax optimal regret

ilde O(d\sqrt{H^3K})

reinforcement learning linear MDPs regret minimization weighted regression variance estimator

2022-12-12abs ↗pdf ↗

Rarely-switching linear bandits: optimization of causal effects for the real worldstat.ML

Excessively changing policies in many real world scenarios is difficult, unethical, or expensive. After all, doctor guidelines, tax codes, and price lists can only be reprinted so often. We may thus want to only change a policy when it is probable that the change is beneficial. In cases that a policy is a threshold on …

2019-05-30abs ↗pdf ↗

Contextual Slate GLM Bandits with Limited Adaptivitycs.LG

Efficient algorithms for contextual slate bandits with limited adaptivity.

problem Contextual slate bandit problem with limited adaptivity.

method Proposed B-SlateGLinCB and RS-SlateGLinCB algorithms for batched and rarely-switching settings.

result Achieved regret bounds of O(Nd^(3/2)√T) and O(Nd√T) under diversity assumption.

contextual bandits generalized linear models limited adaptivity slate selection regret bounds

2026-06-30abs ↗pdf ↗

Actor-Critics Can Achieve Optimal Sample Efficiencystat.ML

New actor-critic algorithm achieves optimal sample efficiency in RL.

problem Achieving

ε

-optimal policies with minimal samples in RL.

method Integrates optimism, off-policy critic estimation, and rare-switching policy resets.

result Sample complexity of

O(dH^5 \log|\mathcal{A}|/ε^2 + d H^4 \log|\mathcal{F}|/ ε^2)

trajectories.

Actor-Critic Reinforcement Learning Optimism Off-Policy Rare-Switching

2025-05-06abs ↗pdf ↗

Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learningcs.LG

We develop a robust RL algorithm for off-dynamics environments with improved suboptimality bounds and computational efficiency.

problem Learning policies robust to uncertainties in transition dynamics between training and deployment environments.

method Distributionally robust Markov decision processes (DRMDPs) with a novel algorithm We-DRIVE-U.

result Improved suboptimality bound of

\widetilde{\mathcal{O}}\big({d H \cdot \min \{1/ρ, H\}/\sqrt{K} }\big)

, near-optimal up to

\mathcal{O}(\sqrt{H})

Reinforcement Learning Distributionally Robust Off-Dynamics Markov Decision Processes Suboptimality Bounds

2024-09-30abs ↗pdf ↗